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Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 10001–10025 of 10307 papers

TitleStatusHype
Adaptive Learning to Speed-Up Control of Prosthetic Hands: a Few Things Everybody Should Know—0
Visual Translation Embedding Network for Visual Relation DetectionCode0
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation—0
Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models—0
Collaborative Deep Reinforcement LearningCode0
Domain Adaptation for Visual Applications: A Comprehensive Survey—0
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network—0
MR-based synthetic CT generation using a deep convolutional neural network methodCode0
Concept Drift Adaptation by Exploiting Historical Knowledge—0
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)—0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization—0
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasksCode0
Question Answering through Transfer Learning from Large Fine-grained Supervision DataCode0
Transfer from Multiple Linear Predictive State Representations (PSR)—0
Relative Camera Pose Estimation Using Convolutional Neural NetworksCode0
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples—0
PCA-Initialized Deep Neural Networks Applied To Document Image Analysis—0
PathNet: Evolution Channels Gradient Descent in Super Neural NetworksCode0
Deep Reinforcement Learning: An OverviewCode0
Greedy Structure Learning of Hierarchical Compositional Models—0
Transfer learning for multi-center classification of chronic obstructive pulmonary disease—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Heterogeneous domain adaptation: An unsupervised approach—0
Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection—0
AENet: Learning Deep Audio Features for Video AnalysisCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified